基于碳排放和分类用户满意度的冷链物流路径优化

Cold Chain Logistics Path Optimization Based on Carbon Emission Classification and User Satisfaction

企业需要在考虑分类用户满意度的前提下降低企业配送成本。同时基于碳中和概念,面对可能的碳税和碳限政策,也需要制定合理的方案来降低企业成本。针对冷链物流路径优化问题,构建了考虑分类用户满意度和碳税碳限政策的冷链物流成本模型,融合遗传算法和蚁群算法设计出Aco-Aga和Aga-Aco算法,并分别对模型优化效果进行对比,Aco-Aga算法收敛更快,解质量更高。实证分析表明模型有效降低了企业碳排放,提高了用户满意度。

Enterprises need to reduce their distribution costs on the premise of considering classified user satisfaction.At the same time,based on the concept of carbon neutralization,in the face of possible carbon tax and carbon limit policies,they also need to formulate reasonable schemes to reduce their costs.Facing the problem of cold chain logistics path optimization,this paper constructs a cold chain logistics cost model considering classified user satisfaction and carbon tax and carbon limit policies,The fusion algorithm of genetic algorithm and ant colony algorithm,Aco-Aga and Aga-Aco algorithm are designed,and the models are optimized respectively.After comparison,it is found that Aco-Aga algorithm converges faster and the solution quality is higher in this model.After obtaining the optimal solution,it is found that the model effectively reduces enterprise carbon emission and improves user satisfaction.